When should I pick Qdrant over Weaviate?

Pick Qdrant if: You need an open-source store with heavy filtering or hybrid search, self-hosted or managed.

When should I pick Weaviate over Qdrant?

Pick Weaviate if: You need an open-source store with heavy filtering or hybrid search, self-hosted or managed.

Which is cheaper, Qdrant or Weaviate?

At vectors stored 1 million vectors, queries 1 million queries, vectors written or updated 500k writes: Qdrant $103/mo (Standard (3 nodes, 0.5 vCPU / 4 GiB each)), Weaviate $253/mo (Flex), at list prices.

Which do more makers use for vector database?

Among makers' products we track: Qdrant 18, Weaviate 16.

Qdrant vs Weaviate for vector database: which fits you

Which fits you

Choose Qdrant if

You need an open-source store with heavy filtering or hybrid search, self-hosted or managed

Use it whenYour queries combine similarity with many filters, such as tenant, date and category.

Trade-offOne more service to deploy and keep in sync with your main database.

Choose Weaviate if

You need an open-source store with heavy filtering or hybrid search, self-hosted or managed

Use it whenUsers search with both exact terms and meaning, and you want both in one query.

Trade-offMore concepts and configuration to learn than simpler stores; some advanced features need a license key.

At a glance

	Qdrant	Weaviate
Used by	18 makers' products · 68 open-source projects	16 makers' products · 20 open-source projects
Cost at default usagevectors stored 1 million vectors, queries 1 million queries, vectors written or updated 500k writes	$103/mo Standard (3 nodes, 0.5 vCPU / 4 GiB each)	$253/mo Flex
Downloads	696.8k/wk−19% vs npm	372.6k/wk−65% vs npm
Pricing	Free and open source to self-host; Qdrant Cloud has a free tier plus usage-based paid plans. · paid from Usage-based, no minimum	Core engine free to self-host (BSD-3-Clause); Weaviate Cloud has an always-free tier and pay-as-you-go plans from $45/month; some advanced features require a license key. · paid from $45/mo
Free tier	Yes	Yes
Open source	Yes · self-hostable	Yes · self-hostable

Cost as you grow

Both cost $0 up to 100k vectors; from 500k vectors Qdrant costs less ($68 vs $126); and still does at 100M vectors ($8,747 vs $25,259).

Weaviate Qdrantx: vectors stored (1,536 dimensions, about 6 gb per million) (million vectors), other usage scaled with it · cheapest usable plan at each point, list prices · try your own numbers

The numbers, plan by plan

Vectors stored (1,536 dimensions, about 6 GB per million)	Qdrant	Weaviate
0.1	$0 Free	$0 Free
0.5	$68 Standard (1 node, 1 vCPU / 8 GiB)	$126 Flex
1	$103 Standard (3 nodes, 0.5 vCPU / 4 GiB each)	$253 Flex
5	$410 Standard (3 nodes, 2 vCPU / 16 GiB each)	$1,263 Flex
10	$820 Standard (3 nodes, 4 vCPU / 32 GiB each)	$2,526 Flex
50	$4,374 Standard (2 nodes, 32 vCPU / 256 GiB each)	$12,629 Flex
100	$8,747 Standard (4 nodes, 32 vCPU / 256 GiB each)	$25,259 Flex

From each vendor's pricing page: Qdrant, Weaviate.

What makers say

Makers on using it for vector database, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.

On Qdrant

After evaluating a bunch of Vector DBs to be our internal vector DB, we finally closed on QDrant because it was the one that scaled the best and had the best price performance ratio

Conva.AI, the makerSep 2026 ↗

Thanks to Qdrant, we utilize it as a vector database to store our knowledge base and uploaded file data. Our RAG would not be possible without it.

AICamp, the makerSep 2026 ↗

We evaluated a bunch of vector DBs—and Qdrant stood out for its blazing speed, filtering, and hybrid search. It's the unsung hero that lets our AI agents recall and reason across docs, CRMs, and conversations in milliseconds.

Zams, the makerSep 2026 ↗

12 more on the Qdrant page →

On Weaviate

To store all our vector embeddings, now a staple for us to build forward. Their automatic 'load balancing' on which vectors are recently used is a game changer for system optimization

Quantera.ai, the makerSep 2026 ↗

Unbody is built on top of Weaviate, making Unbody content API run 100% on a vector database. Weaviate modular architecture as well as user-friendly GraphQl API has played a vital role in our product.

Unbody, the makerSep 2026 ↗

Weaviate gives us fast, semantic search across unstructured call data, crucial for surfacing insights in real time. Its native vector support and scalability made it the best fit for building an AI native platform like Insight7.

Insight7, the makerSep 2026 ↗

5 more on the Weaviate page →

Loved and watch-outs

Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.

Qdrant

Most loved

It is fast and scales with strong price-performance, helped by its Rust core. PH
Payload filtering and hybrid semantic plus boolean search work together. PH
Runs easily self-hosted in Docker, a common pick for local RAG and agent memory. PH HN HN 2 HN 3

Watch-outs

As a separate server it is slower than in-process stores for small local datasets. HN
Setting up and operating a vector database is overkill for teams that just need working search. HN HN 2

On Product Hunt: 5.0★, 23 reviews · mentioned most: fast performance, semantic search, excellent documentation

Weaviate

Most loved

Open source and free to run, with good docs and an active, friendly community. HN weaviate.io PH HN 2
Scales to production workloads, including multi-tenant setups on its managed cloud. weaviate.io PH PH 2 weaviate.io 2
Built-in vectorization and a GraphQL API let queries mix semantic search with structured data. PH HN HN 2

Watch-outs